This invention relates generally to the field of fingerprint identification/verification systems. More particularly, this invention relates to a fingerprint identification/verification system using two dimensional bitmaps instead of traditional feature extraction.
Two types of matching applications are used for fingerprints. One-to-one verification is used to compare a fingerprint with either a particular template stored on, for example, a smart card, or a template recovered from a database by having the person provide his or her name, Personal Identification Number (PIN) code, or the like. One-to-many identification is used to compare a fingerprint to a database of templates, and is required when a person presents only his or her finger which is then compared to a number of stored images.
Traditional fingerprint identification by feature extraction has been used by institutions like the Federal Bureau of Investigation (FBI) for identifying criminals and is the most common fingerprint identification system. In feature extraction, the pattern of a fingerprint is checked for any special `features` such as ridge bifurcations (splits) and ridge endings amongst the meandering ridges of the fingerprint. Once each such feature is identified, the location, that is, the distance and direction between the features, and perhaps the orientation of each feature, is determined. By storing only the feature location information, a smaller amount of data can be stored compared to storing the complete fingerprint pattern. However, by extracting and storing only the location of each feature, that is, the one-dimensional point on the fingerprint where the feature is located and, perhaps, the type of feature, information for security purposes is lost because all of the non-feature information is then unavailable for comparisons (matching).
Also, in order to determine the absolute location of the features, an unambiguous starting point (reference point) is selected for the fingerprint. Traditional methods locate a `core point` as the reference point. This core point is usually selected according to different criteria depending on the type of fingerprint, for example, whorl, circular or other type. Thus, a fingerprint in such a traditional system must first be classified as a known type before the core point can be determined and the features located.
Another difficulty encountered with automated fingerprint identification or verification systems is the inability of the system to differentiate between a real fingerprint, that is, a fingerprint on a finger, and an image or plastic model of a fingerprint. In traditional systems the type of sensor can help, for example, a heat sensor to detect body heat, but these sensors can be defeated.
In addition, identification presents difficulties when the database of possible fingerprints becomes quite large. In traditional fingerprint systems, each type of fingerprint is categorized and the types of features provide additional subclasses. Nevertheless, the number of classes and subclasses is quite small when compared to the number of fingerprints which may be in any particular class or subclass. Also, once a class or subclass is selected, possible matches in a different class or subclass of the same level of the hierarchy are not checked. Thus, for fingerprints which do not clearly fall within a particular class or subclass, there may be stored fingerprints in the database which are not checked. Accordingly, a search for a matching fingerprint image on file can be both time consuming and result in a false indication that the particular fingerprint is not on file.